df <- read.csv("kbopitchingdata.csv")
# View(df)
str(df)'data.frame': 323 obs. of 34 variables:
$ id : int 1 2 3 4 5 6 7 8 9 10 ...
$ year : int 2021 2021 2021 2021 2021 2021 2021 2021 2021 2021 ...
$ team : chr "LG Twins" "KT Wiz" "Doosan Bears" "Samsung Lions" ...
$ average_age : num 26.3 28.4 27.5 28.8 27.7 25.8 27.3 27 25.3 27.1 ...
$ runs_per_game : num 3.9 4.06 4.57 4.57 4.8 4.89 5.13 5.13 5.22 5.64 ...
$ wins : int 72 75 70 75 67 69 66 49 58 64 ...
$ losses : int 57 59 65 59 67 67 63 82 75 71 ...
$ win_loss_percentage: num 0.558 0.56 0.519 0.56 0.5 0.507 0.512 0.374 0.436 0.474 ...
$ ERA : num 3.57 3.67 4.28 4.29 4.5 4.33 4.8 4.67 4.89 5.39 ...
$ run_average_9 : num 3.96 4.17 4.66 4.7 4.95 5.02 5.2 5.29 5.33 5.75 ...
$ games : int 143 143 143 143 143 143 143 143 143 143 ...
$ games_started : int 143 143 143 143 143 143 143 143 143 143 ...
$ games_finished : int 143 141 141 141 140 142 143 142 142 142 ...
$ complete_game : int 0 2 2 2 3 1 0 1 1 1 ...
$ shutouts : int 18 6 10 14 10 7 5 6 6 12 ...
$ saves : int 32 33 27 46 33 30 25 21 36 36 ...
$ innings_pitched : num 1264 1255 1260 1250 1247 ...
$ hits : int 1117 1166 1288 1287 1256 1276 1283 1200 1274 1330 ...
$ runs : int 557 581 653 653 686 699 733 734 746 806 ...
$ earned_runs : int 501 512 599 596 624 604 676 648 684 756 ...
$ home_runs : int 79 85 104 129 122 100 147 114 133 132 ...
$ walks : int 542 486 586 526 585 566 623 669 616 653 ...
$ intentional_walks : int 17 18 16 13 14 27 27 22 5 19 ...
$ strikeouts : int 1062 1051 1037 1031 1046 893 1006 1006 946 1047 ...
$ hit_batter : int 97 42 73 51 77 80 78 101 104 86 ...
$ balks : int 5 1 7 3 8 4 9 11 9 5 ...
$ wild_pitches : int 43 56 51 56 74 58 40 56 58 102 ...
$ batters_faced : int 5416 5359 5596 5496 5575 5568 5661 5633 5658 5722 ...
$ WHIP : num 1.31 1.32 1.49 1.45 1.48 ...
$ hits_9 : num 8 8.4 9.2 9.3 9.1 9.2 9.1 8.6 9.1 9.5 ...
$ homeruns_9 : num 0.6 0.6 0.7 0.9 0.9 0.7 1 0.8 1 0.9 ...
$ walks_9 : num 3.9 3.5 4.2 3.8 4.2 4.1 4.4 4.8 4.4 4.7 ...
$ strikeouts_9 : num 7.6 7.5 7.4 7.4 7.5 6.4 7.1 7.2 6.8 7.5 ...
$ strikeout_walk : num 1.96 2.16 1.77 1.96 1.79 1.58 1.61 1.5 1.54 1.6 ...
# 결측치가 있어서 필요 없는 컬럼 제거
df_01 <- subset(df, select=-c(games_started,games_finished,intentional_walks, balks, wild_pitches))
# 연도별 바뀐 팀들을 현대의 이름으로 재정렬
for (i in (1:length(df_01$team))){
if(df_01$team[i] == 'MBC Blue Dragons'){
df_01$team[i] = 'LG Twins'
} else if(df_01$team[i] == 'OB Bears'){
df_01$team[i] = 'Doosan Bears'
} else if(df_01$team[i] == 'Nexen Heroes' | df_01$team[i] == 'Woori Heroes'){
df_01$team[i] = 'Kiwoom Heroes'
} else if(df_01$team[i] == 'SK Wyverns'){
df_01$team[i] = 'SSG Landers'
} else if(df_01$team[i] == 'Binggre Eagles'){
df_01$team[i] = 'Hanwha Eagles'
} else if(df_01$team[i] == 'Haitai Tigers'){
df_01$team[i] = 'Kia Tigers'
} else if(df_01$team[i] == 'Pacific Dolphins' | df_01$team[i] == 'Chungbo Pintos' | df_01$team[i] == 'Sammi Superstars'){
df_01$team[i] = 'Hyundai Unicorns'
}
}